Overview

Dataset statistics

Number of variables22
Number of observations68784
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.4 MiB
Average record size in memory341.6 B

Variable types

Categorical5
Numeric17

Warnings

b_size has constant value "0" Constant
id has a high cardinality: 1099 distinct values High cardinality
width is highly correlated with heightHigh correlation
height is highly correlated with widthHigh correlation
p is highly correlated with framesHigh correlation
frames is highly correlated with pHigh correlation
p_size is highly correlated with sizeHigh correlation
size is highly correlated with p_sizeHigh correlation
o_width is highly correlated with o_heightHigh correlation
o_height is highly correlated with o_widthHigh correlation
o_codec is highly correlated with b_sizeHigh correlation
o_framerate is highly correlated with b_sizeHigh correlation
b_size is highly correlated with o_codec and 2 other fieldsHigh correlation
codec is highly correlated with b_sizeHigh correlation
b is highly skewed (γ1 = 28.04883379) Skewed
o_framerate is uniformly distributed Uniform
b has 67925 (98.8%) zeros Zeros

Reproduction

Analysis started2021-03-04 06:35:19.341768
Analysis finished2021-03-04 06:36:13.131552
Duration53.79 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

id
Categorical

HIGH CARDINALITY

Distinct1099
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
4Fob-eSnrb8
 
841
1LXH-OibJYQ
 
841
1Vq6-f1kYeM
 
841
15fr-zSdCqg
 
841
3o_2-igmsyo
 
841
Other values (1094)
64579 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters756624
Distinct characters64
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1018 ?
Unique (%)1.5%

Sample

1st row04t6-jw9czg
2nd row04t6-jw9czg
3rd row04t6-jw9czg
4th row04t6-jw9czg
5th row04t6-jw9czg
ValueCountFrequency (%)
4Fob-eSnrb8841
 
1.2%
1LXH-OibJYQ841
 
1.2%
1Vq6-f1kYeM841
 
1.2%
15fr-zSdCqg841
 
1.2%
3o_2-igmsyo841
 
1.2%
28BV-NZV3C8841
 
1.2%
1YDt-FkeZ4E841
 
1.2%
0OUmDeBapbQ841
 
1.2%
4keb-__zqyQ841
 
1.2%
1tos-YhVzA4841
 
1.2%
Other values (1089)60374
87.8%
2021-03-03T22:36:13.398798image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1y-y-rhcxrq841
 
1.2%
1ydt-fkez4e841
 
1.2%
0icm-cu4_ae841
 
1.2%
28bv-nzv3c8841
 
1.2%
1kxw-xa6qkq841
 
1.2%
3ayz-pca1ai841
 
1.2%
3n6c-updnas841
 
1.2%
3_ff9rtycq0841
 
1.2%
22ue-ouubh8841
 
1.2%
1s9d-jtkyuw841
 
1.2%
Other values (1089)60374
87.8%

Most occurring characters

ValueCountFrequency (%)
-73896
 
9.8%
023744
 
3.1%
323670
 
3.1%
222818
 
3.0%
121991
 
2.9%
Y20373
 
2.7%
A17881
 
2.4%
417494
 
2.3%
c15837
 
2.1%
I15382
 
2.0%
Other values (54)503538
66.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter278440
36.8%
Lowercase Letter244056
32.3%
Decimal Number152535
20.2%
Dash Punctuation73896
 
9.8%
Connector Punctuation7697
 
1.0%

Most frequent character per category

ValueCountFrequency (%)
c15837
 
6.5%
k14508
 
5.9%
s13687
 
5.6%
e11923
 
4.9%
z11922
 
4.9%
b11908
 
4.9%
o11125
 
4.6%
a11097
 
4.5%
y10719
 
4.4%
g10324
 
4.2%
Other values (16)121006
49.6%
ValueCountFrequency (%)
Y20373
 
7.3%
A17881
 
6.4%
I15382
 
5.5%
T15264
 
5.5%
Q14619
 
5.3%
U13674
 
4.9%
E12822
 
4.6%
J11908
 
4.3%
D11557
 
4.2%
W10725
 
3.9%
Other values (16)134235
48.2%
ValueCountFrequency (%)
023744
15.6%
323670
15.5%
222818
15.0%
121991
14.4%
417494
11.5%
613592
8.9%
811134
7.3%
57705
 
5.1%
96050
 
4.0%
74337
 
2.8%
ValueCountFrequency (%)
-73896
100.0%
ValueCountFrequency (%)
_7697
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin522496
69.1%
Common234128
30.9%

Most frequent character per script

ValueCountFrequency (%)
Y20373
 
3.9%
A17881
 
3.4%
c15837
 
3.0%
I15382
 
2.9%
T15264
 
2.9%
Q14619
 
2.8%
k14508
 
2.8%
s13687
 
2.6%
U13674
 
2.6%
E12822
 
2.5%
Other values (42)368449
70.5%
ValueCountFrequency (%)
-73896
31.6%
023744
 
10.1%
323670
 
10.1%
222818
 
9.7%
121991
 
9.4%
417494
 
7.5%
613592
 
5.8%
811134
 
4.8%
57705
 
3.3%
_7697
 
3.3%
Other values (2)10387
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII756624
100.0%

Most frequent character per block

ValueCountFrequency (%)
-73896
 
9.8%
023744
 
3.1%
323670
 
3.1%
222818
 
3.0%
121991
 
2.9%
Y20373
 
2.7%
A17881
 
2.4%
417494
 
2.3%
c15837
 
2.1%
I15382
 
2.0%
Other values (54)503538
66.6%

duration
Real number (ℝ≥0)

Distinct1086
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean286.4139214
Minimum31.08
Maximum25844.086
Zeros0
Zeros (%)0.0%
Memory size537.5 KiB
2021-03-03T22:36:13.542506image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum31.08
5-th percentile42.2
Q1106.765
median239.14166
Q3379.32
95-th percentile750.433
Maximum25844.086
Range25813.006
Interquartile range (IQR)272.555

Descriptive statistics

Standard deviation287.25765
Coefficient of variation (CV)1.002945837
Kurtosis921.6556529
Mean286.4139214
Median Absolute Deviation (MAD)140.17834
Skewness12.67999923
Sum19700695.17
Variance82516.95747
MonotocityNot monotonic
2021-03-03T22:36:14.101051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
714.371841
 
1.2%
1768.9417841
 
1.2%
106.765841
 
1.2%
37.011665841
 
1.2%
452.689841
 
1.2%
167.39166841
 
1.2%
222.53334841
 
1.2%
43.827841
 
1.2%
177.33333841
 
1.2%
326.58832841
 
1.2%
Other values (1076)60374
87.8%
ValueCountFrequency (%)
31.081
< 0.1%
31.1151
< 0.1%
31.1331
< 0.1%
31.2066672
< 0.1%
31.3466661
< 0.1%
31.5821
< 0.1%
31.7751
< 0.1%
31.8329981
< 0.1%
32.2671
< 0.1%
32.3671
< 0.1%
ValueCountFrequency (%)
25844.0861
< 0.1%
5881.2421
< 0.1%
3628.76831
< 0.1%
3242.66671
< 0.1%
3115.3031
< 0.1%
2789.2771
< 0.1%
2783.1331
< 0.1%
2709.8331
< 0.1%
2704.5351
< 0.1%
2554.441
< 0.1%

codec
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
h264
31545 
vp8
18387 
mpeg4
12012 
flv
6840 

Length

Max length5
Median length4
Mean length3.807876832
Min length3

Characters and Unicode

Total characters261921
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmpeg4
2nd rowmpeg4
3rd rowmpeg4
4th rowmpeg4
5th rowmpeg4
ValueCountFrequency (%)
h26431545
45.9%
vp818387
26.7%
mpeg412012
 
17.5%
flv6840
 
9.9%
2021-03-03T22:36:14.371326image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-03-03T22:36:14.470016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
h26431545
45.9%
vp818387
26.7%
mpeg412012
 
17.5%
flv6840
 
9.9%

Most occurring characters

ValueCountFrequency (%)
443557
16.6%
h31545
12.0%
231545
12.0%
631545
12.0%
p30399
11.6%
v25227
9.6%
818387
7.0%
m12012
 
4.6%
e12012
 
4.6%
g12012
 
4.6%
Other values (2)13680
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter136887
52.3%
Decimal Number125034
47.7%

Most frequent character per category

ValueCountFrequency (%)
h31545
23.0%
p30399
22.2%
v25227
18.4%
m12012
 
8.8%
e12012
 
8.8%
g12012
 
8.8%
f6840
 
5.0%
l6840
 
5.0%
ValueCountFrequency (%)
443557
34.8%
231545
25.2%
631545
25.2%
818387
14.7%

Most occurring scripts

ValueCountFrequency (%)
Latin136887
52.3%
Common125034
47.7%

Most frequent character per script

ValueCountFrequency (%)
h31545
23.0%
p30399
22.2%
v25227
18.4%
m12012
 
8.8%
e12012
 
8.8%
g12012
 
8.8%
f6840
 
5.0%
l6840
 
5.0%
ValueCountFrequency (%)
443557
34.8%
231545
25.2%
631545
25.2%
818387
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII261921
100.0%

Most frequent character per block

ValueCountFrequency (%)
443557
16.6%
h31545
12.0%
231545
12.0%
631545
12.0%
p30399
11.6%
v25227
9.6%
818387
7.0%
m12012
 
4.6%
e12012
 
4.6%
g12012
 
4.6%
Other values (2)13680
 
5.2%

width
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean624.9341707
Minimum176
Maximum1920
Zeros0
Zeros (%)0.0%
Memory size537.5 KiB
2021-03-03T22:36:14.578764image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum176
5-th percentile176
Q1320
median480
Q3640
95-th percentile1280
Maximum1920
Range1744
Interquartile range (IQR)320

Descriptive statistics

Standard deviation463.169069
Coefficient of variation (CV)0.7411485732
Kurtosis0.8991506193
Mean624.9341707
Median Absolute Deviation (MAD)160
Skewness1.336629494
Sum42985472
Variance214525.5865
MonotocityNot monotonic
2021-03-03T22:36:14.678459image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
48017916
26.0%
32013649
19.8%
17612012
17.5%
128011569
16.8%
64010226
14.9%
19203412
 
5.0%
ValueCountFrequency (%)
17612012
17.5%
32013649
19.8%
48017916
26.0%
64010226
14.9%
128011569
16.8%
19203412
 
5.0%
ValueCountFrequency (%)
19203412
 
5.0%
128011569
16.8%
64010226
14.9%
48017916
26.0%
32013649
19.8%
17612012
17.5%

height
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean412.5722261
Minimum144
Maximum1080
Zeros0
Zeros (%)0.0%
Memory size537.5 KiB
2021-03-03T22:36:14.790198image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum144
5-th percentile144
Q1240
median360
Q3480
95-th percentile720
Maximum1080
Range936
Interquartile range (IQR)240

Descriptive statistics

Standard deviation240.6154718
Coefficient of variation (CV)0.5832081186
Kurtosis0.7101434916
Mean412.5722261
Median Absolute Deviation (MAD)120
Skewness1.094027481
Sum28378368
Variance57895.80526
MonotocityNot monotonic
2021-03-03T22:36:14.885942image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
36017916
26.0%
24013649
19.8%
14412012
17.5%
72011569
16.8%
48010226
14.9%
10803412
 
5.0%
ValueCountFrequency (%)
14412012
17.5%
24013649
19.8%
36017916
26.0%
48010226
14.9%
72011569
16.8%
10803412
 
5.0%
ValueCountFrequency (%)
10803412
 
5.0%
72011569
16.8%
48010226
14.9%
36017916
26.0%
24013649
19.8%
14412012
17.5%

bitrate
Real number (ℝ≥0)

Distinct1095
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean693701.5
Minimum8384
Maximum7628466
Zeros0
Zeros (%)0.0%
Memory size537.5 KiB
2021-03-03T22:36:15.014598image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum8384
5-th percentile52783
Q1134334
median291150
Q3652967
95-th percentile2759449
Maximum7628466
Range7620082
Interquartile range (IQR)518633

Descriptive statistics

Standard deviation1095627.555
Coefficient of variation (CV)1.579393377
Kurtosis11.45931628
Mean693701.5
Median Absolute Deviation (MAD)233585
Skewness3.203007293
Sum4.771556398 × 1010
Variance1.200399739 × 1012
MonotocityNot monotonic
2021-03-03T22:36:15.166193image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51082841
 
1.2%
849586841
 
1.2%
56152841
 
1.2%
29096841
 
1.2%
95903841
 
1.2%
5992818841
 
1.2%
559834841
 
1.2%
538452841
 
1.2%
794075841
 
1.2%
297373841
 
1.2%
Other values (1085)60374
87.8%
ValueCountFrequency (%)
83841
 
< 0.1%
110191
 
< 0.1%
242511
 
< 0.1%
26968841
1.2%
273441
 
< 0.1%
276561
 
< 0.1%
280831
 
< 0.1%
29096841
1.2%
313371
 
< 0.1%
352371
 
< 0.1%
ValueCountFrequency (%)
76284661
 
< 0.1%
62114711
 
< 0.1%
60531941
 
< 0.1%
60109421
 
< 0.1%
60001011
 
< 0.1%
5999648841
1.2%
59994361
 
< 0.1%
5992818841
1.2%
59300001
 
< 0.1%
58983841
 
< 0.1%

framerate
Real number (ℝ≥0)

Distinct261
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.24132053
Minimum5.7057524
Maximum48
Zeros0
Zeros (%)0.0%
Memory size537.5 KiB
2021-03-03T22:36:15.340731image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5.7057524
5-th percentile12
Q115
median25.02174
Q329
95-th percentile30.05252
Maximum48
Range42.2942476
Interquartile range (IQR)14

Descriptive statistics

Standard deviation7.224847956
Coefficient of variation (CV)0.3108621968
Kurtosis-0.7880005723
Mean23.24132053
Median Absolute Deviation (MAD)3.97826
Skewness-0.8391563359
Sum1598630.991
Variance52.19842798
MonotocityNot monotonic
2021-03-03T22:36:15.480362image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2912840
18.7%
1211167
16.2%
2511052
16.1%
306015
 
8.7%
152551
 
3.7%
232530
 
3.7%
24858
 
1.2%
7845
 
1.2%
13843
 
1.2%
16842
 
1.2%
Other values (251)19241
28.0%
ValueCountFrequency (%)
5.7057524841
1.2%
64
 
< 0.1%
6.00123741
 
< 0.1%
6.0138891
 
< 0.1%
6.07042261
 
< 0.1%
6.16494851
 
< 0.1%
7845
1.2%
81
 
< 0.1%
106
 
< 0.1%
10.0281121
 
< 0.1%
ValueCountFrequency (%)
481
< 0.1%
391
< 0.1%
351
< 0.1%
30.81251
< 0.1%
30.6363641
< 0.1%
30.5348831
< 0.1%
30.51
< 0.1%
30.4615381
< 0.1%
30.4393941
< 0.1%
30.406251
< 0.1%

i
Real number (ℝ≥0)

Distinct306
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.8683124
Minimum7
Maximum5170
Zeros0
Zeros (%)0.0%
Memory size537.5 KiB
2021-03-03T22:36:15.696735image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile15
Q139
median80
Q3138
95-th percentile256
Maximum5170
Range5163
Interquartile range (IQR)99

Descriptive statistics

Standard deviation84.76479079
Coefficient of variation (CV)0.8403510357
Kurtosis224.6402734
Mean100.8683124
Median Absolute Deviation (MAD)43
Skewness5.64781843
Sum6938126
Variance7185.069758
MonotocityNot monotonic
2021-03-03T22:36:15.865285image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
372538
 
3.7%
231695
 
2.5%
511694
 
2.5%
151691
 
2.5%
531688
 
2.5%
1121688
 
2.5%
1101685
 
2.4%
771684
 
2.4%
871684
 
2.4%
1131684
 
2.4%
Other values (296)51053
74.2%
ValueCountFrequency (%)
710
 
< 0.1%
810
 
< 0.1%
913
 
< 0.1%
10849
1.2%
119
 
< 0.1%
126
 
< 0.1%
13847
1.2%
14845
1.2%
151691
2.5%
16845
1.2%
ValueCountFrequency (%)
51701
< 0.1%
31391
< 0.1%
19881
< 0.1%
19461
< 0.1%
16081
< 0.1%
15261
< 0.1%
14451
< 0.1%
14441
< 0.1%
14011
< 0.1%
13731
< 0.1%

p
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1042
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6531.69221
Minimum175
Maximum304959
Zeros0
Zeros (%)0.0%
Memory size537.5 KiB
2021-03-03T22:36:16.058800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum175
5-th percentile870.9
Q12374
median5515
Q39155
95-th percentile17128
Maximum304959
Range304784
Interquartile range (IQR)6781

Descriptive statistics

Standard deviation6075.871744
Coefficient of variation (CV)0.9302140315
Kurtosis99.3937665
Mean6531.69221
Median Absolute Deviation (MAD)3424
Skewness3.958072832
Sum449275917
Variance36916217.45
MonotocityNot monotonic
2021-03-03T22:36:16.215381image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6457842
 
1.2%
3271842
 
1.2%
7541842
 
1.2%
4646842
 
1.2%
6726842
 
1.2%
7366841
 
1.2%
2374841
 
1.2%
1144841
 
1.2%
3257841
 
1.2%
10924841
 
1.2%
Other values (1032)60369
87.8%
ValueCountFrequency (%)
1751
 
< 0.1%
2481
 
< 0.1%
2741
 
< 0.1%
3261
 
< 0.1%
334841
1.2%
3421
 
< 0.1%
3431
 
< 0.1%
3512
 
< 0.1%
3661
 
< 0.1%
3672
 
< 0.1%
ValueCountFrequency (%)
3049591
< 0.1%
1731231
< 0.1%
917581
< 0.1%
827361
< 0.1%
819691
< 0.1%
798511
< 0.1%
796121
< 0.1%
678101
< 0.1%
624891
< 0.1%
533951
< 0.1%

b
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.147854152
Minimum0
Maximum9407
Zeros67925
Zeros (%)98.8%
Memory size537.5 KiB
2021-03-03T22:36:16.391876image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9407
Range9407
Interquartile range (IQR)0

Descriptive statistics

Standard deviation92.51617705
Coefficient of variation (CV)10.11342939
Kurtosis2060.539711
Mean9.147854152
Median Absolute Deviation (MAD)0
Skewness28.04883379
Sum629226
Variance8559.243015
MonotocityNot monotonic
2021-03-03T22:36:16.547461image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
067925
98.8%
704841
 
1.2%
94071
 
< 0.1%
26261
 
< 0.1%
15391
 
< 0.1%
61
 
< 0.1%
19961
 
< 0.1%
68061
 
< 0.1%
6741
 
< 0.1%
12511
 
< 0.1%
Other values (10)10
 
< 0.1%
ValueCountFrequency (%)
067925
98.8%
61
 
< 0.1%
421
 
< 0.1%
1841
 
< 0.1%
3161
 
< 0.1%
6261
 
< 0.1%
6741
 
< 0.1%
704841
 
1.2%
8911
 
< 0.1%
10071
 
< 0.1%
ValueCountFrequency (%)
94071
< 0.1%
68061
< 0.1%
33851
< 0.1%
26261
< 0.1%
24161
< 0.1%
20251
< 0.1%
19961
< 0.1%
19651
< 0.1%
15391
< 0.1%
12511
< 0.1%

frames
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1044
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6641.708377
Minimum192
Maximum310129
Zeros0
Zeros (%)0.0%
Memory size537.5 KiB
2021-03-03T22:36:16.697099image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum192
5-th percentile894
Q12417
median5628
Q39232
95-th percentile17438
Maximum310129
Range309937
Interquartile range (IQR)6815

Descriptive statistics

Standard deviation6153.342453
Coefficient of variation (CV)0.9264698333
Kurtosis100.9157284
Mean6641.708377
Median Absolute Deviation (MAD)3500
Skewness3.966427912
Sum456843269
Variance37863623.34
MonotocityNot monotonic
2021-03-03T22:36:16.874584image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2862842
 
1.2%
1318842
 
1.2%
1184842
 
1.2%
2318842
 
1.2%
1050842
 
1.2%
1716842
 
1.2%
7636841
 
1.2%
12266841
 
1.2%
2417841
 
1.2%
6843841
 
1.2%
Other values (1034)60368
87.8%
ValueCountFrequency (%)
1921
 
< 0.1%
2661
 
< 0.1%
3011
 
< 0.1%
344841
1.2%
3511
 
< 0.1%
3541
 
< 0.1%
3611
 
< 0.1%
3641
 
< 0.1%
3731
 
< 0.1%
3741
 
< 0.1%
ValueCountFrequency (%)
3101291
< 0.1%
1762621
< 0.1%
933661
< 0.1%
836781
< 0.1%
834951
< 0.1%
812961
< 0.1%
810561
< 0.1%
690381
< 0.1%
638621
< 0.1%
543661
< 0.1%

i_size
Real number (ℝ≥0)

Distinct1099
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2838986.702
Minimum11648
Maximum90828552
Zeros0
Zeros (%)0.0%
Memory size537.5 KiB
2021-03-03T22:36:17.073625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum11648
5-th percentile48267
Q1393395
median945865
Q33392479
95-th percentile12989828
Maximum90828552
Range90816904
Interquartile range (IQR)2999084

Descriptive statistics

Standard deviation4325136.594
Coefficient of variation (CV)1.52347899
Kurtosis15.27907687
Mean2838986.702
Median Absolute Deviation (MAD)834015
Skewness2.833497796
Sum1.952768613 × 1011
Variance1.870680656 × 1013
MonotocityNot monotonic
2021-03-03T22:36:17.221227image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
182297841
 
1.2%
33046841
 
1.2%
1382103841
 
1.2%
47521841
 
1.2%
5611970841
 
1.2%
47585841
 
1.2%
9132637841
 
1.2%
671936841
 
1.2%
2444863841
 
1.2%
1451634841
 
1.2%
Other values (1089)60374
87.8%
ValueCountFrequency (%)
116481
< 0.1%
118961
< 0.1%
137631
< 0.1%
137671
< 0.1%
140671
< 0.1%
140941
< 0.1%
141111
< 0.1%
143801
< 0.1%
156831
< 0.1%
171001
< 0.1%
ValueCountFrequency (%)
908285521
< 0.1%
900886791
< 0.1%
818954741
< 0.1%
804815331
< 0.1%
619619701
< 0.1%
618296411
< 0.1%
616656781
< 0.1%
568177261
< 0.1%
525817971
< 0.1%
475716161
< 0.1%

p_size
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1099
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22180569.3
Minimum33845
Maximum768996980
Zeros0
Zeros (%)0.0%
Memory size537.5 KiB
2021-03-03T22:36:17.379804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum33845
5-th percentile448322
Q11851539
median6166260
Q315155062
95-th percentile79210885
Maximum768996980
Range768963135
Interquartile range (IQR)13303523

Descriptive statistics

Standard deviation50973061.32
Coefficient of variation (CV)2.298095267
Kurtosis21.26095612
Mean22180569.3
Median Absolute Deviation (MAD)4932842
Skewness4.404562767
Sum1.525668279 × 1012
Variance2.598252981 × 1015
MonotocityNot monotonic
2021-03-03T22:36:17.598220image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2066350841
 
1.2%
6254165841
 
1.2%
14806840841
 
1.2%
5760936841
 
1.2%
856907841
 
1.2%
448322841
 
1.2%
47579961841
 
1.2%
6660941841
 
1.2%
11352574841
 
1.2%
778937841
 
1.2%
Other values (1089)60374
87.8%
ValueCountFrequency (%)
338451
< 0.1%
364111
< 0.1%
552971
< 0.1%
697711
< 0.1%
887751
< 0.1%
957401
< 0.1%
1009511
< 0.1%
1065371
< 0.1%
1136801
< 0.1%
1371381
< 0.1%
ValueCountFrequency (%)
7689969801
< 0.1%
7177190111
< 0.1%
5605152661
< 0.1%
5485952311
< 0.1%
4886214061
< 0.1%
4679984511
< 0.1%
4478301541
< 0.1%
4390699231
< 0.1%
4299356711
< 0.1%
4089069591
< 0.1%

b_size
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
0
68784 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68784
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
068784
100.0%
2021-03-03T22:36:17.898028image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-03-03T22:36:17.990781image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
068784
100.0%

Most occurring characters

ValueCountFrequency (%)
068784
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number68784
100.0%

Most frequent character per category

ValueCountFrequency (%)
068784
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common68784
100.0%

Most frequent character per script

ValueCountFrequency (%)
068784
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII68784
100.0%

Most frequent character per block

ValueCountFrequency (%)
068784
100.0%

size
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1099
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25022942.37
Minimum191879
Maximum806711069
Zeros0
Zeros (%)0.0%
Memory size537.5 KiB
2021-03-03T22:36:18.102442image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum191879
5-th percentile596349
Q12258222
median7881069
Q319773349
95-th percentile82698405
Maximum806711069
Range806519190
Interquartile range (IQR)17515127

Descriptive statistics

Standard deviation54144015.39
Coefficient of variation (CV)2.163774931
Kurtosis20.83196075
Mean25022942.37
Median Absolute Deviation (MAD)6358145
Skewness4.344797622
Sum1.721178068 × 1012
Variance2.931574403 × 1015
MonotocityNot monotonic
2021-03-03T22:36:18.293970image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
334032841
 
1.2%
3773200841
 
1.2%
19773349841
 
1.2%
5470732841
 
1.2%
8627672841
 
1.2%
798230841
 
1.2%
9697521841
 
1.2%
10941484841
 
1.2%
56821235841
 
1.2%
7447655841
 
1.2%
Other values (1089)60374
87.8%
ValueCountFrequency (%)
1918791
< 0.1%
2060021
< 0.1%
2117101
< 0.1%
2132201
< 0.1%
2152161
< 0.1%
2168981
< 0.1%
2289741
< 0.1%
2299041
< 0.1%
2332891
< 0.1%
2360091
< 0.1%
ValueCountFrequency (%)
8067110691
< 0.1%
7703008081
< 0.1%
5884124001
< 0.1%
5862457611
< 0.1%
5146316921
< 0.1%
4925137941
< 0.1%
4838968161
< 0.1%
4753950131
< 0.1%
4662172051
< 0.1%
4451488121
< 0.1%

o_codec
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
mpeg4
17291 
vp8
17277 
flv
17135 
h264
17081 

Length

Max length5
Median length3
Mean length3.75109037
Min length3

Characters and Unicode

Total characters258015
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmpeg4
2nd rowmpeg4
3rd rowmpeg4
4th rowmpeg4
5th rowmpeg4
ValueCountFrequency (%)
mpeg417291
25.1%
vp817277
25.1%
flv17135
24.9%
h26417081
24.8%
2021-03-03T22:36:18.615110image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-03-03T22:36:18.721826image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
mpeg417291
25.1%
vp817277
25.1%
flv17135
24.9%
h26417081
24.8%

Most occurring characters

ValueCountFrequency (%)
p34568
13.4%
v34412
13.3%
434372
13.3%
m17291
6.7%
e17291
6.7%
g17291
6.7%
817277
6.7%
f17135
6.6%
l17135
6.6%
h17081
6.6%
Other values (2)34162
13.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter172204
66.7%
Decimal Number85811
33.3%

Most frequent character per category

ValueCountFrequency (%)
p34568
20.1%
v34412
20.0%
m17291
10.0%
e17291
10.0%
g17291
10.0%
f17135
10.0%
l17135
10.0%
h17081
9.9%
ValueCountFrequency (%)
434372
40.1%
817277
20.1%
217081
19.9%
617081
19.9%

Most occurring scripts

ValueCountFrequency (%)
Latin172204
66.7%
Common85811
33.3%

Most frequent character per script

ValueCountFrequency (%)
p34568
20.1%
v34412
20.0%
m17291
10.0%
e17291
10.0%
g17291
10.0%
f17135
10.0%
l17135
10.0%
h17081
9.9%
ValueCountFrequency (%)
434372
40.1%
817277
20.1%
217081
19.9%
617081
19.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII258015
100.0%

Most frequent character per block

ValueCountFrequency (%)
p34568
13.4%
v34412
13.3%
434372
13.3%
m17291
6.7%
e17291
6.7%
g17291
6.7%
817277
6.7%
f17135
6.6%
l17135
6.6%
h17081
6.6%
Other values (2)34162
13.2%

o_bitrate
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1395035.953
Minimum56000
Maximum5000000
Zeros0
Zeros (%)0.0%
Memory size537.5 KiB
2021-03-03T22:36:18.849445image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum56000
5-th percentile56000
Q1109000
median539000
Q33000000
95-th percentile5000000
Maximum5000000
Range4944000
Interquartile range (IQR)2891000

Descriptive statistics

Standard deviation1749351.506
Coefficient of variation (CV)1.253983098
Kurtosis-0.1935935618
Mean1395035.953
Median Absolute Deviation (MAD)430000
Skewness1.177295744
Sum9.5956153 × 1010
Variance3.060230692 × 1012
MonotocityNot monotonic
2021-03-03T22:36:19.005073image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
560009855
14.3%
1090009835
14.3%
50000009830
14.3%
30000009827
14.3%
5390009824
14.3%
2420009821
14.3%
8200009792
14.2%
ValueCountFrequency (%)
560009855
14.3%
1090009835
14.3%
2420009821
14.3%
5390009824
14.3%
8200009792
14.2%
30000009827
14.3%
50000009830
14.3%
ValueCountFrequency (%)
50000009830
14.3%
30000009827
14.3%
8200009792
14.2%
5390009824
14.3%
2420009821
14.3%
1090009835
14.3%
560009855
14.3%

o_framerate
Categorical

HIGH CORRELATION
UNIFORM

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
15.0
13772 
12.0
13764 
29.97
13759 
25.0
13751 
24.0
13738 

Length

Max length5
Median length4
Mean length4.200031984
Min length4

Characters and Unicode

Total characters288895
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12.0
2nd row12.0
3rd row12.0
4th row12.0
5th row12.0
ValueCountFrequency (%)
15.013772
20.0%
12.013764
20.0%
29.9713759
20.0%
25.013751
20.0%
24.013738
20.0%
2021-03-03T22:36:19.307260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-03-03T22:36:19.396027image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
15.013772
20.0%
12.013764
20.0%
29.9713759
20.0%
25.013751
20.0%
24.013738
20.0%

Most occurring characters

ValueCountFrequency (%)
.68784
23.8%
055025
19.0%
255012
19.0%
127536
9.5%
527523
9.5%
927518
9.5%
713759
 
4.8%
413738
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number220111
76.2%
Other Punctuation68784
 
23.8%

Most frequent character per category

ValueCountFrequency (%)
055025
25.0%
255012
25.0%
127536
12.5%
527523
12.5%
927518
12.5%
713759
 
6.3%
413738
 
6.2%
ValueCountFrequency (%)
.68784
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common288895
100.0%

Most frequent character per script

ValueCountFrequency (%)
.68784
23.8%
055025
19.0%
255012
19.0%
127536
9.5%
527523
9.5%
927518
9.5%
713759
 
4.8%
413738
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII288895
100.0%

Most frequent character per block

ValueCountFrequency (%)
.68784
23.8%
055025
19.0%
255012
19.0%
127536
9.5%
527523
9.5%
927518
9.5%
713759
 
4.8%
413738
 
4.8%

o_width
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean802.3363573
Minimum176
Maximum1920
Zeros0
Zeros (%)0.0%
Memory size537.5 KiB
2021-03-03T22:36:19.522645image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum176
5-th percentile176
Q1320
median480
Q31280
95-th percentile1920
Maximum1920
Range1744
Interquartile range (IQR)960

Descriptive statistics

Standard deviation609.959797
Coefficient of variation (CV)0.7602295364
Kurtosis-0.7947632439
Mean802.3363573
Median Absolute Deviation (MAD)160
Skewness0.8140700438
Sum55187904
Variance372050.9539
MonotocityNot monotonic
2021-03-03T22:36:19.616438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
17611474
16.7%
32011473
16.7%
48011467
16.7%
64011460
16.7%
192011459
16.7%
128011451
16.6%
ValueCountFrequency (%)
17611474
16.7%
32011473
16.7%
48011467
16.7%
64011460
16.7%
128011451
16.6%
192011459
16.7%
ValueCountFrequency (%)
192011459
16.7%
128011451
16.6%
64011460
16.7%
48011467
16.7%
32011473
16.7%
17611474
16.7%

o_height
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean503.8255408
Minimum144
Maximum1080
Zeros0
Zeros (%)0.0%
Memory size537.5 KiB
2021-03-03T22:36:19.782990image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum144
5-th percentile144
Q1240
median360
Q3720
95-th percentile1080
Maximum1080
Range936
Interquartile range (IQR)480

Descriptive statistics

Standard deviation315.9704381
Coefficient of variation (CV)0.6271425573
Kurtosis-0.7526445433
Mean503.8255408
Median Absolute Deviation (MAD)120
Skewness0.7042375416
Sum34655136
Variance99837.31775
MonotocityNot monotonic
2021-03-03T22:36:19.888695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
14411474
16.7%
24011473
16.7%
36011467
16.7%
48011460
16.7%
108011459
16.7%
72011451
16.6%
ValueCountFrequency (%)
14411474
16.7%
24011473
16.7%
36011467
16.7%
48011460
16.7%
72011451
16.6%
108011459
16.7%
ValueCountFrequency (%)
108011459
16.7%
72011451
16.6%
48011460
16.7%
36011467
16.7%
24011473
16.7%
14411474
16.7%

umem
Real number (ℝ≥0)

Distinct9395
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228224.7179
Minimum22508
Maximum711824
Zeros0
Zeros (%)0.0%
Memory size537.5 KiB
2021-03-03T22:36:20.025302image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum22508
5-th percentile132192
Q1216820
median219480
Q3219656
95-th percentile330259.4
Maximum711824
Range689316
Interquartile range (IQR)2836

Descriptive statistics

Standard deviation97430.87837
Coefficient of variation (CV)0.4269076517
Kurtosis12.23992984
Mean228224.7179
Median Absolute Deviation (MAD)1680
Skewness3.210103971
Sum1.5698209 × 1010
Variance9492776061
MonotocityNot monotonic
2021-03-03T22:36:20.171947image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21948012746
18.5%
2168206862
 
10.0%
2196565579
 
8.1%
2211604875
 
7.1%
2211523813
 
5.5%
2189323454
 
5.0%
1657002693
 
3.9%
2170802080
 
3.0%
2194641445
 
2.1%
2186641086
 
1.6%
Other values (9385)24151
35.1%
ValueCountFrequency (%)
225081
< 0.1%
226041
< 0.1%
231322
< 0.1%
233481
< 0.1%
236681
< 0.1%
238681
< 0.1%
239321
< 0.1%
244561
< 0.1%
246321
< 0.1%
249281
< 0.1%
ValueCountFrequency (%)
7118241
< 0.1%
7116641
< 0.1%
7114241
< 0.1%
7110721
< 0.1%
7109001
< 0.1%
7108841
< 0.1%
7105761
< 0.1%
7105561
< 0.1%
7103481
< 0.1%
7100761
< 0.1%

utime
Real number (ℝ≥0)

Distinct10960
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.996354821
Minimum0.184
Maximum224.574
Zeros0
Zeros (%)0.0%
Memory size537.5 KiB
2021-03-03T22:36:20.335514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.184
5-th percentile0.72
Q12.096
median4.408
Q310.433
95-th percentile39.3454
Maximum224.574
Range224.39
Interquartile range (IQR)8.337

Descriptive statistics

Standard deviation16.1074286
Coefficient of variation (CV)1.611330218
Kurtosis25.39045693
Mean9.996354821
Median Absolute Deviation (MAD)2.968
Skewness4.245501978
Sum687589.27
Variance259.4492559
MonotocityNot monotonic
2021-03-03T22:36:20.480163image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.96465
 
0.1%
1.2465
 
0.1%
1.24465
 
0.1%
1.28862
 
0.1%
1.2661
 
0.1%
0.96858
 
0.1%
0.98857
 
0.1%
0.67256
 
0.1%
1.23255
 
0.1%
1.05655
 
0.1%
Other values (10950)68185
99.1%
ValueCountFrequency (%)
0.1841
 
< 0.1%
0.2521
 
< 0.1%
0.2562
 
< 0.1%
0.263
 
< 0.1%
0.2645
 
< 0.1%
0.26810
 
< 0.1%
0.27218
< 0.1%
0.27616
< 0.1%
0.2830
< 0.1%
0.28425
< 0.1%
ValueCountFrequency (%)
224.5741
< 0.1%
214.4811
< 0.1%
203.7811
< 0.1%
198.261
< 0.1%
194.961
< 0.1%
192.2641
< 0.1%
190.2961
< 0.1%
189.51
< 0.1%
187.141
< 0.1%
183.3631
< 0.1%

Interactions

2021-03-03T22:35:25.269285image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:25.470757image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:25.632320image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:25.806874image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:25.956453image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:26.106164image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:26.259750image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:26.406358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:26.562916image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:26.705542image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:26.866340image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:27.029895image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:27.181490image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:27.333109image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:27.474702image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:27.638286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:27.783880image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:27.939487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:28.092073image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:28.261627image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:28.411226image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:28.567800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:28.725361image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:28.876957image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:29.045506image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:29.203103image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:29.380610image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:29.554146image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:29.709732image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:29.871302image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:30.038812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:30.246212image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:30.416763image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:30.574330image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:30.729914image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:30.890485image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:31.028126image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:31.173737image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:31.339345image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:31.480953image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:31.635538image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:31.781117image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:31.949310image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:32.108899image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:32.251571image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:32.402167image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:32.537812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:32.689385image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:32.826043image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:32.995525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:33.199048image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:33.375560image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:33.545107image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:33.720588image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:33.893164image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:34.056752image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:34.227277image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:34.394825image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:34.586310image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:34.764853image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:34.933383image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:35.114860image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:35.279457image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:35.462967image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:35.629528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:36.057378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:36.201010image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:36.335640image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:36.490216image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:36.624883image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:36.765508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:36.904125image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:37.053729image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:37.196152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:37.353752image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:37.523310image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:37.667921image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:37.821456image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:37.962136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:38.115709image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:38.261336image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:38.412906image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:38.573484image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:38.716104image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:38.881660image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:39.021288image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:39.170888image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:39.309541image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:39.464099image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:39.605725image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:39.766296image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:39.925880image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:40.071475image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:40.225094image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:40.369701image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:40.528274image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:40.670901image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:40.823469image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:40.990039image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:41.139663image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:41.346109image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:41.509637image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:41.691185image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:41.859732image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:42.046203image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:42.226765image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:42.431174image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:42.654604image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:42.828152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:43.008631image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:43.195163image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:43.379692image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:43.612355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:43.852368image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:44.033900image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:44.207389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:44.396919image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:44.570419image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:44.761907image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:44.955396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:45.146905image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:45.332434image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:45.570745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:45.778230image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-03-03T22:35:49.668229image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:49.832785image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:49.976408image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-03-03T22:35:50.272573image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:50.418261image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-03T22:35:50.584782image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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Correlations

2021-03-03T22:36:20.666667image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-03T22:36:21.020725image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-03T22:36:21.351879image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-03T22:36:21.680016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-03T22:36:21.966245image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-03T22:36:12.122252image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-03T22:36:12.778457image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

iddurationcodecwidthheightbitrateframerateipbframesi_sizep_sizeb_sizesizeo_codeco_bitrateo_framerateo_widtho_heightumemutime
004t6-jw9czg130.35667mpeg41761445459012.027153701564644838250540889537mpeg45600012.0176144225080.612
104t6-jw9czg130.35667mpeg41761445459012.027153701564644838250540889537mpeg45600012.0320240251640.980
204t6-jw9czg130.35667mpeg41761445459012.027153701564644838250540889537mpeg45600012.0480360292281.216
304t6-jw9czg130.35667mpeg41761445459012.027153701564644838250540889537mpeg45600012.0640480343161.692
404t6-jw9czg130.35667mpeg41761445459012.027153701564644838250540889537mpeg45600012.01280720585283.456
504t6-jw9czg130.35667mpeg41761445459012.027153701564644838250540889537mpeg45600012.0192010801020726.320
604t6-jw9czg130.35667mpeg41761445459012.027153701564644838250540889537mpeg45600015.0176144231320.728
704t6-jw9czg130.35667mpeg41761445459012.027153701564644838250540889537mpeg45600015.0320240251640.944
804t6-jw9czg130.35667mpeg41761445459012.027153701564644838250540889537mpeg45600015.0480360292361.476
904t6-jw9czg130.35667mpeg41761445459012.027153701564644838250540889537mpeg45600015.0640480343121.964

Last rows

iddurationcodecwidthheightbitrateframerateipbframesi_sizep_sizeb_sizesizeo_codeco_bitrateo_framerateo_widtho_heightumemutime
68774zTW-C8JRdVA376.44168mpeg41761445609312.00000076444104517430710220876302639473vp85600012.001280720881567.420
68775zu8f-JRkS-w77.36000h26464048080075025.00000040189401934734147700910607743253mpeg424200024.001920108011860015.781
68776ZvgM-o1AdQY418.72000flv32024027549625.00000022410245010469181750412601985014419489h26424200015.00128072030711222.385
68777ZV-n-6QtlCY128.06100flv32024025721729.00000066377303839743566337388004117446h264500000012.00176144884442.840
68778zVPS-xZhPwE198.15700h264640480115323129.000000106572905835167847826886625028565103vp882000015.00640480884448.957
68779ZWEN-71BqPs972.27100h26448036027882229.00000056028580029140732462826561730033886358flv24200024.00640480886921.552
68780zWQN-bqqg0o129.88100vp864048063933130.162790363855038918757849503846010379630mpeg453900029.971920108010752418.557
68781zX17-vi0sqQ249.68000vp832024035934525.06827412961130624217586649456514011215178flv53900012.00176144887080.752
68782zyiT-TzxIpk183.62334h2641280720284753929.00000098540505503524629460113035065359329mpeg453900012.00320240887245.444
68783zZKo-QsY86U294.61334mpeg41761445524212.0000006134740353584002195040902034411h26482000024.00176144887363.076